Genes and Environment

January 27, 2012

In my Biology and Cognitive Science of Communication course last year, one of the #1 themes–one of perhaps 5 ideas that I really hoped students would understand by the end of the course and take away with them–is the notion that genes/innate biology and environment have, for the most part, mutual effects on outcomes (including/especially behavior) that cannot be parceled out in to X% and Y%.  That is, the interaction between genes and environment is usually much more important than either by itself.

Of course, this is not always true.  There are extreme versions of both–single-gene traits (e.g., color blindness, Huntington’s Disease, many others) and trisomies (e.g., Down Syndrome) obviously have a very large, direct effect on outcomes.  Likewise, extreme environmental circumstances (e.g., Fetal Alcohol Syndrome, head trauma, sensory deprivation) can have large, direct effects.

But for the most part, it’s an interaction–and that’s an easy, glib line to memorize, but I think it took students awhile (and, for that matter, it took me a long time) to really grasp what that means.  But this morning I was perusing my new* results and I came across a good example.  Below the cut is copy pasta of one of my results, “Response to Diet.”  This result reports on 3 SNPs (essentially, genes) that research shows to be associated with the link between diet and obesity.

Now, calls these “preliminary results” because each link only has one approved study that goes with it, and there are a host of statistical issues with the way that much research is done on gene effects (basically, it’s correlational data mining, so unless you have MANY studies showing the same association repeatedly, there’s always the possibility that a given result is a chance fluke rather than an actual relationship).  But let’s pretend for a moment that they’re associations that can be taken at face value.

Each of these genes has 3 known variants (this is not the only way it can happen, but it’s usual for the SNPs that 23andme reports).  Scientists doing gene studies compute odds of whatever outcome they’re interested in (e.g., obesity, Parkinson’s Disease, etc.), comparing the less usual variants (i.e., mutations) to the most common variant (i.e., “normal”).  Sometimes the mutation seems to be protective–it does better than the most common variant (e.g., it is correlated with lower odds of heart disease than the general population), but often the mutation is associated with higher odds of things you don’t want.

The results below the cut are interesting because they specifically relate to response to diet–that is, how my body interacts with the environment. Thus, these aren’t “obesity” genes in the simplistic sense–having a given variant causes obesity–but they are (perhaps) genes that determine how I metabolize different kinds of foods, and thus what kinds of foods are likely to help me lose weight or make me gain weight.**

So the genes themselves are not causing weight loss/gain.  Neither is my diet alone.  You can’t say it’s 30% genes and 70% environment.  It’s genes AND diet (environment).  The effects of the genes are conditional upon what food I eat, and the effect of food I eat is conditional upon my genes.  Their effects cannot be separated.

I think this is also interesting to interpret the wide variety of fad diets out there, and how so many people can swear that a given fad diet is THE way to lose weight because it worked very, very well for them.  How can Atkins work for some people, and low-fat diets work for other people?  I suggest that those people have different genes like the ones below (assuming this research pans out).  This isn’t even that earth-shattering of an idea–maybe some people store carbs more easily than others, whereas some people store fats more easily than others.  Human metabolism is ridiculously complex–you learn the Krebs cycle in high-school or freshman Bio and think that’s complex?  That’s just baby stuff, and really scientists have only scratched the surface of figuring out how we do the biochemical wonders that we do.***  It’s no surprise, to me at least, that the wide range of individual differences in diet efficacy could have some underlying genetic cause.

And my particular results affirm why I have personally had success with low-fat diets and not with low-carb diets (that, and I’m also a carrier for MCAD deficiency–which apparently even carriers have lower-than-typical MCAD levels, so eating very few carbs tends to do things like make me faint).

* If you are not familiar, is a company that, for a fee and a container of spit, tells you about your genes.  They only provide results that are based on some baseline level of expert-vetted (as well as peer-reviewed, published) research.  For that reason, though their information about my genes doesn’t change, they periodically update what given results MEAN based on new research.  You can also download the full result that lists every gene that they genotyped, which despite the fact that it is not a full sequence, is 8MB of text–that’s a lot.

** I phrase things this way because I am currently, um, larger than I would prefer to be–curse you, baby weight that never left!!–but the reverse is obviously also true if you are one of those folks who have trouble keeping weight on.

*** You know what’s scary, that I did not know prior to being married to someone in Big Pharma?  A great many drugs on the market have unknown or only theorized mechanisms.  They don’t really know how they work.  Drugs are developed by testing a bunch of molecules (usually, ones that have something in common with another drug/compound that is already known to work) to see which ones are biologically active, and out of those which ones do the things that we want with the fewest side effects (in a very simplistic nutshell).  It’s very much “throw stuff, see what sticks.”

[See below for my 23andme “Response to Diet” results]

Response to Diet


What you eat has a huge impact on your health, but how you respond to your diet is influenced by many factors. Researchers are learning that genetics plays a large role in how people perceive flavors and in their eating behaviors. Genetics also influences how your body metabolizes and uses different foods, perhaps helping to explain why some people can eat as much as they want and never gain weight while others can’t seem to lose weight despite their best efforts.

Last Updated

January 19th, 2012

The report “Response to Diet and Exercise” can now be found in two separate reports specific to response to diet (this report) and response to exercise.

Gene/SNP Summaries
Benefit from monounsaturated fat
Journal Br J Nutr
Study Size
Replications None
Contrary Studies None
Applicable Ethnicities European
Marker rs1801282

Several studies have shown that rs1801282 (also known as the Pro12Ala variant in the PPARG gene) influences whether an individual benefits from a diet high in monounsaturated fat. (This type of diet is often called a “Mediterranean”-style diet because olive oil is a good source of monounsaturated fat.) In people of European descent carrying at least one copy of the G version of rs1801282, increasing intake of just monounsaturated fat was associated with reductions in body mass index (BMI). In addition, a low fat diet led to an increase in waist circumference in people with the G version but a diet high in monounsaturated fat protected against this effect. The protection was even stronger in people with type 2 diabetes.

Who Genotype What It Means
GG A low fat diet may lead to increased waist circumference but a diet high in monounsaturated fat protects against increased waist circumference and may lead to reductions in BMI.
CG A low fat diet may lead to increased waist circumference but a diet high in monounsaturated fat protects against increased waist circumference and may lead to reductions in BMI.
Amber Westcott-Baker CC A diet high in monounsaturated fat is not likely to have beneficial effects on BMI or waist circumference.
Razquin C et al. (2009) . “The Mediterranean diet protects against waist circumference enlargement in 12Ala carriers for the PPARgamma gene: 2 years’ follow-up of 774 subjects at high cardiovascular risk.” Br J Nutr 102(5):672-9.
Memisoglu A et al. (2003) . “Interaction between a peroxisome proliferator-activated receptor gamma gene polymorphism and dietary fat intake in relation to body mass.” Hum Mol Genet 12(22):2923-9.
Effect of saturated fat on obesity risk
Journal Arch Intern Med
Study Size
Replications None
Contrary Studies None
Applicable Ethnicities European
Marker rs5082

A study of about 3,500 people with mainly European ancestry showed that having two copies of the G version of rs5082 was associated with increased odds of obesity in those who ate a diet high in saturated fat. In people who consumed a diet low in saturated fat, rs5082 did not have an effect on risk of obesity.

Who Genotype What It Means
GG Increased odds of obesity on a high saturated fat diet. No increase in odds of obesity on a low saturated fat diet.
Amber Westcott-Baker AG Typical odds of obesity on both a high and low saturated fat diet.
AA Typical odds of obesity on both a high and low saturated fat diet.
Corella D et al. (2009) . “APOA2, dietary fat, and body mass index: replication of a gene-diet interaction in 3 independent populations.” Arch Intern Med 169(20):1897-906.
Effect of fat intake on body mass index (BMI)
Journal J Mol Med
Study Size
Replications None
Contrary Studies None
Applicable Ethnicities European
Marker rs662799

A study of more than 2,000 people of European descent found that among those who consumed more than 30% of their calories from fat, having two copies of the A version of rs662799 was associated with higher BMI compared to having one or no copies. The variant had no effect among those who consumed less than 30% of their calories from fat.

Who Genotype What It Means
Amber Westcott-Baker AA A high fat diet (30% of calories from fat) is associated with higher BMI.
AG Dietary fat consumption is not associated with changes in BMI.
GG Dietary fat consumption is not associated with changes in BMI.
Corella D et al. (2007) . “APOA5 gene variation modulates the effects of dietary fat intake on body mass index and obesity risk in the Framingham Heart Study.” J Mol Med 85(2):119-28.

The genotyping services of 23andMe are performed in LabCorp’s CLIA-certified laboratory. The tests have not been cleared or approved by the FDA but have been analytically validated according to CLIA standards.


2 Responses to “Genes and Environment”

  1. In form:

    I think if I knew who your audience was with this, I’d have an easier time forming proper criticism for it. The tone is pleasantly professional, though I was a bit thrown by the use of the term “copypasta”, and near the end you seem to be talking directly to your students, with the “the Krebs cycle is baby stuff”.

    In content:

    It seems very wise to approach this sort of mapping from the elevated information-theory perspective of correlating huge batches of studies on the one hand with huge samples of genomes on the other, though I get the impression that a massive amount of clarifying work has to be done to avoid false positive correlations.

    For example, let’s assume a very good case, and say 1000 studies with 1000 participants each reveal that those with the ABC variant of the XYZ gene are much more likely to have serious reactions to mold spores. That’s a nice correlation but it still makes no solid statement about the role that the XYZ gene plays in the proceedings. Perhaps the ABC variant of the XYZ gene causes a cascade of other gene expressions that appear semi-random in the data, and what we’re actually looking at is three overlapping sets of positive correlations, in genes we haven’t even considered before, and the XYZ gene is only culpable in that it caused those three to express, for myriad reasons. We’re still looking through a glass darkly here, and this is leaving out all the potential data landmines of geographic distribution of ethnicity versus geographic distribution of climate and mold species that we can only hope were avoided through excellent study design and scope.

    It all reminds me a bit of the movie GATTACA. The tickertape spews out of the machine and suddenly we’re “dead at 29 from heart failure”, except we’re not really, and we the audience know it, because the machine doesn’t know jack shit about what’s going to happen to us and our collection of gene snippets over three decades as we plow through the world, expressing this or that or the other thing. With something as complex as diet, I expect the same is true. We can read and heed the warning to “avoid a high fat diet”, all the way up until we realize that A. the warning should have been about something else that was only tangential to fat content or B. the foods we THOUGHT were low in fat actually WEREN’T because our nutritionist, chef, butcher, or farmer, somewhere along the line, made a miscalculation or made a bad assumption, or C. something we did in the past 5 years has altered our physiology (disease, deficiency, immune response, etc) and the advice is worthless.

    It all screams for a better approach.

    I suspect that where this will all truly lead is to a cheap electronic service that will take a sample of your base genome at the outset and then painlessly measure a myriad of blood conditions and the content of your presently expressed genes from some standard portion of the body, and then present you with a list of suggestions that data-mining (of your past and of others’) has predicted will help you meet your goals, personal-assistant style.

    For example,
    “I’m planning to have a child in one year. What should I do to maximize its health?”
    “… ** BEEEP ** Well, you know those baked potatoes you’ve eaten for the past 6 months? Try and swap those for broccoli.”
    “I hate broccoli. What else can I do?”
    “…. ** BEEEP ** Swap them for yams instead. You seem to like yams.”

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